End-to-End Motion Capture for Arbitrary Skeletons

Recent methods for arbitrary-skeleton motion capture from monocular video [Gong et al. 2026] follow a factorized pipeline, where a Video-to-Pose network predicts joint positions and an analytical inverse-kinematics (IK) stage recovers joint rotations. While effective, this design is inherently limited, since joint positions do not fully determine rotations and leave degrees of freedom such as bone-axis twist ambiguous, and meanwhile the non-differentiable IK stage prevents the system from adapting to noisy predictions or optimizing for the final animation objective. In this work, we instead present the first fully end-to-end framework, where both Video-to-Pose and Pose-to-Rotation are learnable and jointly optimized. Crucially, we observe that the ambiguity in pose-to-rotation mapping arises from missing coordinate-system information, as the same joint positions can correspond to different rotations under different rest poses and local axis conventions, and to resolve this we introduce a reference pose-rotation pair from the target asset, which together with the rest pose not only anchors the mapping but also defines the underlying rotation coordinate system. This turns rotation prediction into a well-constrained conditional problem and enables effective learning. In addition, our model predicts joint positions directly from video without relying on mesh intermediates, which improves both robustness and efficiency, and both stages share a skeleton-aware Global-Local Graph-Guided Multi-Head Attention (GL-GMHA) module for joint-level local reasoning and global coordination. Experiments on Truebones Zoo and Objaverse show that our method reduces rotation error from ~17° to ~10°, and to 6.54° on unseen skeletons, while achieving ~20× faster inference than mesh-based pipelines. Code, data, and an interactive Hugging Face demo will be available at https://animotionlab.github.io/MoCapAnythingV2/.